Resources

• This activity draws on Chapter 1 in Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

What are R and RStudio?

• In the quantitative methods tutorial, we will use R and RStudio to illustrate basic statistical concepts
• R is a programming language and free computing environment for statistical computing and graphics
• RStudio is an integrated development environment (IDE) that provides an interface by adding many convenient features and tools
• Think of R as a car’s engine and RStudio as the dashboard

Installing R

• Download and install R by going to https://cloud.r-project.org/
  • Windows: Click on “Download R for Windows”, then click on “base”, then click on the Download link
  • macOS user: Click on “Download R for macOS”, then under “Latest release:” click on R-X.X.X.pkg, where R-X.X.X is the version number
  • Linux user: Click on “Download R for Linux” and choose your distribution for more information on installing R for your setup

Installing RStudio

• Download and install RStudio at https://www.rstudio.com/products/rstudio/download/
  • Scroll down to “Installers for Supported Platforms” near the bottom of the page
  • Click on the download link corresponding to your computer’s operating system

Using R via RStudio

• Using R by opening RStudio

Using R via RStudio

• After you open RStudio, you should see something like this

Peronalization

• Customize the appearance of your R IDE by going to Tools -> Global Options -> Appearance

Layout

• Rstudio contains 4 panes: source, console, environment, and viewer
• The left pane is the source where you write code
• Try typing 2 + 2 into the source panel and hitting ENTER
  • The console panel pop-up in the bottom-left and return a value of 4

[INSERT SCREEN RECORDING]

Layout

• The top-right includes the Environment panel
  • This contains information about any objects that you load into your environment
  • Type sum = 2 + 2 into the source and hit ENTER
  • The object sum should appear in your environment
• The bottom-right includes the Viewer panel, where you will be able to see graphics that you generate

Basic programming concepts

• Running code: telling R to perform an act by giving it commands in the source or console
• Objects: where values are saved in R. On the last slide, you created the object sum.
• Data types:
  • Integers are whole numbers like -1, 0, 2, 4092
  • Doubles are integers with decimal values like -24.932 and 0.8
  • Logicals are either TRUE or FALSE
  • Characters are text or strings like "hello world" and "welcome to R"

Basic programming concepts

• Vectors are a series of values. These are created using the c() function. For example, c(6, 11, 13, 31) creates a four element vector of integers.
• Factors are a group of characters/strings with a fixed number of unique values
• Data frames are objects where the rows correspond to observations and the columns correspond to variables that describe the observations

Basic programming concepts

• Conditionals:
  • Testing for equality in R is done using ==. For example, 2 + 1 == 3 will return TRUE
  • Boolean algebra: Operators such as < (less than), <= (less than or equal), and !=(not equal to). For example, 3 + 5 <= 1 will return FALSE
  • Logical operators: & represent “and” while | represents “or.” For example, (2 + 1 == 3) & (2 + 1 == 4) returns FALSE since both clauses are not TRUE

Loading Packages

• R gives you accesss to thousands of “packages” that are created by users
• Packages contain datasets and bundles of code called “functions” that can execute specific tasks
• Use install.packages() to install a package
  • Insert the name of the package contained in quotation marks
  • Start by installing the dplyr package

Loading Data

• Load data into your environment by “reading-in” a spreadsheet
• Spreadsheets should be saved as a .csv file
• Use read.csv() to pull data from a spreadsheet on your harddrive into your R/RStudio environment
  • Within the parentheses, add the full file pathway where the .csv file is stored

[INSERT SCREEN RECORDING]

Errors, warnings, and messages

• When you run code, R often provides feedback in the console
• There are 3 main types of feedback that appear in RED
  • Errors: When the RED text is prefaced with “Error in…”, your code will not work. The error message will try to explain the problem.
  • Warnings: When the red text is prefaced with “Warning:”, your code will still work, but there still might be a problem. The warning message will try to explain the problem.
  • Messages: When the red text doesn’t start with “Error” or “Warning”, it’s just a friendly message